94 citations · 181 across the 19 of their papers we have counts for
19 papers
Reinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments
Xiao Yang, Lingxuan Wu, Lizhong Wang +3
Adversarial attacks in 3D environments have emerged as a critical threat to the reliability of visual perception systems, particularly in safety-sensitive applications such as iden…
Self-Consistent Model-based Adaptation for Visual Reinforcement Learning
Xinning Zhou, Chengyang Ying, Yao Feng +2
Visual reinforcement learning agents typically face serious performance declines in real-world applications caused by visual distractions. Existing methods rely on fine-tuning the…
Exploratory Diffusion Model for Unsupervised Reinforcement Learning
Chengyang Ying, Huayu Chen, Xinning Zhou +3
Unsupervised reinforcement learning (URL) aims to pre-train agents by exploring diverse states or skills in reward-free environments, facilitating efficient adaptation to downstrea…
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
Chengyang Ying, Zhongkai Hao, Xinning Zhou +4
Designing generalizable agents capable of adapting to diverse embodiments has achieved significant attention in Reinforcement Learning (RL), which is critical for deploying RL agen…
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
Songming Liu, Zhongkai Hao, Chengyang Ying +3
The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly n…
Task Aware Dreamer for Task Generalization in Reinforcement Learning
Chengyang Ying, Xinning Zhou, Zhongkai Hao +4
A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with d…
To Make Yourself Invisible with Adversarial Semantic Contours
Yichi Zhang, Zijian Zhu, Hang Su +4
Modern object detectors are vulnerable to adversarial examples, which may bring risks to real-world applications. The sparse attack is an important task which, compared with the po…
GNOT: A General Neural Operator Transformer for Operator Learning
Zhongkai Hao, Zhengyi Wang, Hang Su +6
Learning partial differential equations' (PDEs) solution operators is an essential problem in machine learning. However, there are several challenges for learning operators in prac…
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
Yao Feng, Yuhong Jiang, Hang Su +2
Model-based reinforcement learning usually suffers from a high sample complexity in training the world model, especially for the environments with complex dynamics. To make the tra…
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
Zhongkai Hao, Songming Liu, Yichi Zhang +4
Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-w…
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
Songming Liu, Zhongkai Hao, Chengyang Ying +3
We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary cond…
Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
Huayu Chen, Cheng Lu, Chengyang Ying +2
In offline reinforcement learning, weighted regression is a common method to ensure the learned policy stays close to the behavior policy and to prevent selecting out-of-sample act…
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
Zhongkai Hao, Chengyang Ying, Hang Su +3
Deep learning based approaches like Physics-informed neural networks (PINNs) and DeepONets have shown promise on solving PDE constrained optimization (PDECO) problems. However, exi…
On the Reuse Bias in Off-Policy Reinforcement Learning
Chengyang Ying, Zhongkai Hao, Xinning Zhou +3
Importance sampling (IS) is a popular technique in off-policy evaluation, which re-weights the return of trajectories in the replay buffer to boost sample efficiency. However, trai…
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Zhongkai Hao, Chengyang Ying, Yinpeng Dong +3
Certified defenses such as randomized smoothing have shown promise towards building reliable machine learning systems against -norm bounded attacks. However, existing metho…
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
Chengyang Ying, Xinning Zhou, Hang Su +3
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observatio…
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model
Jialian Li, Tongzheng Ren, Dong Yan +2
In high-stake scenarios like medical treatment and auto-piloting, it's risky or even infeasible to collect online experimental data to train the agent. Simulation-based training ca…
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors
Zhengyi Wang, Zhongkai Hao, Ziqiao Wang +2
While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks o…
Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning
You Qiaoben, Chengyang Ying, Xinning Zhou +3
Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease a victim's cumulative expected reward by manipulating the victim's observations. Despite…